code: def test_gridsearch_metric_score(self):
      np.random.seed(1)
  
      model_class = Theta
      params = {"theta": list(range(3, 6))}
      dummy_series = get_dummy_series(ts_length=50)
  
      best_model, _, score = model_class.gridsearch(
          params,
          series=dummy_series,
          forecast_horizon=10,
          stride=1,
          start=dummy_series.time_index[-21],
      )
      recalculated_score = best_model.backtest(
          series=dummy_series,
          start=dummy_series.time_index[-21],
          forecast_horizon=10,
          stride=1,
      )
  
      assert (
          score == recalculated_score
      ), "The metric scores should match"
  
imports: import random
  from itertools import product
  import numpy
  import pandas
  import pytest
  from darts import TimeSeries
  from darts.datasets import AirPassengersDataset, MonthlyMilkDataset
  from darts.logging import get_logger
  from darts.metrics import mape, r2_score
  from darts.models import ARIMA, FFT, ExponentialSmoothing, NaiveDrift, NaiveSeasonal, Theta
  from darts.tests.conftest import tfm_kwargs
  from darts.utils.timeseries_generation import gaussian_timeseries
  from darts.utils.timeseries_generation import linear_timeseries
  from darts.utils.timeseries_generation import random_walk_timeseries
  from darts.utils.timeseries_generation import sine_timeseries
label_desc: Use image node_id 6 for calling the TestBacktesting obj's underlying member method code with example usage: obj.test_gridsearch_metric_score() without return types